Results on CUHK-SYSU.
As shown in Table REF , GLCNet and GLCNet+CBGM both outperforms all one-step and two-step person search models without any tricks, such as using additional auxiliary model or data (e.g., knowledge distillation or domain adaptation). Compared with the stat e-of-the-art two-step model TCTS [1]}, our GLCNet+CBG outperforms it by 2.2% and 1.3% w.r.t.mAP and top-1 accuracy, respectively. Compared to the sophisticated tricks adopted by TCTS, e.g., random erasing [2]}, label smooth, and triplet loss [3]}, our approach only adopts random horizontal flip for data augmentation. Our GLCNet model also outperforms the state-of-the-art one-step model SeqNet+CBGM [4]} by 1.3% and 0.6% w.r.t. mAP and top-1 accuracy, respectively. Besides, we simply keep the hyper-parameters of CBGM instead of making adjustment.
Moreover, we illustrate the results of GLCNet with different gallery sizes and make the comparison between our model and both one-step and two-step models. The detailed comparison results are illustrated in Fig. REF . From the figure, we can observe that our GLCNet and GLCNet+CBGM outperform all the existing models by notable margins.
In addition, our approach achieves the best performance in terms of detection on CUHK-SYSU, as shown in Table REF , indicating that our model can handle the conflicting objectives of detection and re-ID during training in a better way.
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